Soil loss estimated by means of the RUSLE model in a subtropical climate watershed

Detalhes bibliográficos
Autor(a) principal: Zanchin,Mayara
Data de Publicação: 2021
Outros Autores: Moura,Maíra Martim de, Nunes,Maria Cândida Moitinho, Beskow,Samuel, Miguel,Pablo, Lima,Cláudia Liane Rodrigues de, Bressiani,Danielle de Almeida
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Revista Brasileira de Ciência do Solo (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832021000100521
Resumo: ABSTRACT Erosion process occurs naturally, shaping the Earth’s surface. Soil loss can cause harmful effects to the environment when intensive anthropic activities occur. Mathematical models have been used as effective and less costly alternatives for identifying sites highly prone to soil loss, especially at the watershed scale. In Brazil, the Revised Universal Soil Loss Equation (RUSLE) is one of the most commonly used soil loss prediction models. The RUSLE requires information on soil erodibility, rainfall erosivity, topography, land use and cover (C), and conservation practices (P) to estimate average annual soil losses. Images derived from remote sensing techniques are generally used to quantify the spatialization of C factor; however, the variation in land use throughout the year is not usually considered. This study aimed to estimate soil losses in an important subwatershed of Candiota river watershed (CRWsub) by using RUSLE, considering land use and rainfall erosivity in different periods of the year. The periods considered were P1 (January, February and March), P2 (April, May and June), P3 (July, August and September) and P4 (October, November and December). Based on the results, the lowest soil losses occurred in P1. Probably, the high vegetation cover in the soil increases its protection against rainfall erosivity. In P3, the heavy rainfall events are predominantly frontal, occurring in the same months as those when the preparation of the soil for later planting takes place; that is, there is no vegetation cover in this period, thus making the soil more prone to erosion. The use of different images to classify and identify land uses is the best way to understand soil losses throughout the year in the study area. It was possible to observe that agricultural areas are generally associated with greater soil losses in the subwatershed. In addition, the land uses were considered to vary quarterly, thereby making it possible to identify the periods most prone to erosion processes throughout the year. Finally, the erosion percentages in the subwatershed can be linked to the tolerance index for different land-uses, soil classes, and slope categories.
id SBCS-1_b69b197f468526f0c7ad513cac7acaa7
oai_identifier_str oai:scielo:S0100-06832021000100521
network_acronym_str SBCS-1
network_name_str Revista Brasileira de Ciência do Solo (Online)
repository_id_str
spelling Soil loss estimated by means of the RUSLE model in a subtropical climate watershedspatio-temporal analysiserosion tolerance indexCandiota riverABSTRACT Erosion process occurs naturally, shaping the Earth’s surface. Soil loss can cause harmful effects to the environment when intensive anthropic activities occur. Mathematical models have been used as effective and less costly alternatives for identifying sites highly prone to soil loss, especially at the watershed scale. In Brazil, the Revised Universal Soil Loss Equation (RUSLE) is one of the most commonly used soil loss prediction models. The RUSLE requires information on soil erodibility, rainfall erosivity, topography, land use and cover (C), and conservation practices (P) to estimate average annual soil losses. Images derived from remote sensing techniques are generally used to quantify the spatialization of C factor; however, the variation in land use throughout the year is not usually considered. This study aimed to estimate soil losses in an important subwatershed of Candiota river watershed (CRWsub) by using RUSLE, considering land use and rainfall erosivity in different periods of the year. The periods considered were P1 (January, February and March), P2 (April, May and June), P3 (July, August and September) and P4 (October, November and December). Based on the results, the lowest soil losses occurred in P1. Probably, the high vegetation cover in the soil increases its protection against rainfall erosivity. In P3, the heavy rainfall events are predominantly frontal, occurring in the same months as those when the preparation of the soil for later planting takes place; that is, there is no vegetation cover in this period, thus making the soil more prone to erosion. The use of different images to classify and identify land uses is the best way to understand soil losses throughout the year in the study area. It was possible to observe that agricultural areas are generally associated with greater soil losses in the subwatershed. In addition, the land uses were considered to vary quarterly, thereby making it possible to identify the periods most prone to erosion processes throughout the year. Finally, the erosion percentages in the subwatershed can be linked to the tolerance index for different land-uses, soil classes, and slope categories.Sociedade Brasileira de Ciência do Solo2021-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832021000100521Revista Brasileira de Ciência do Solo v.45 2021reponame:Revista Brasileira de Ciência do Solo (Online)instname:Sociedade Brasileira de Ciência do Solo (SBCS)instacron:SBCS10.36783/18069657rbcs20210050info:eu-repo/semantics/openAccessZanchin,MayaraMoura,Maíra Martim deNunes,Maria Cândida MoitinhoBeskow,SamuelMiguel,PabloLima,Cláudia Liane Rodrigues deBressiani,Danielle de Almeidaeng2021-12-03T00:00:00Zoai:scielo:S0100-06832021000100521Revistahttp://www.scielo.br/scielo.php?script=sci_serial&pid=0100-0683&lng=es&nrm=isohttps://old.scielo.br/oai/scielo-oai.php||sbcs@ufv.br1806-96570100-0683opendoar:2021-12-03T00:00Revista Brasileira de Ciência do Solo (Online) - Sociedade Brasileira de Ciência do Solo (SBCS)false
dc.title.none.fl_str_mv Soil loss estimated by means of the RUSLE model in a subtropical climate watershed
title Soil loss estimated by means of the RUSLE model in a subtropical climate watershed
spellingShingle Soil loss estimated by means of the RUSLE model in a subtropical climate watershed
Zanchin,Mayara
spatio-temporal analysis
erosion tolerance index
Candiota river
title_short Soil loss estimated by means of the RUSLE model in a subtropical climate watershed
title_full Soil loss estimated by means of the RUSLE model in a subtropical climate watershed
title_fullStr Soil loss estimated by means of the RUSLE model in a subtropical climate watershed
title_full_unstemmed Soil loss estimated by means of the RUSLE model in a subtropical climate watershed
title_sort Soil loss estimated by means of the RUSLE model in a subtropical climate watershed
author Zanchin,Mayara
author_facet Zanchin,Mayara
Moura,Maíra Martim de
Nunes,Maria Cândida Moitinho
Beskow,Samuel
Miguel,Pablo
Lima,Cláudia Liane Rodrigues de
Bressiani,Danielle de Almeida
author_role author
author2 Moura,Maíra Martim de
Nunes,Maria Cândida Moitinho
Beskow,Samuel
Miguel,Pablo
Lima,Cláudia Liane Rodrigues de
Bressiani,Danielle de Almeida
author2_role author
author
author
author
author
author
dc.contributor.author.fl_str_mv Zanchin,Mayara
Moura,Maíra Martim de
Nunes,Maria Cândida Moitinho
Beskow,Samuel
Miguel,Pablo
Lima,Cláudia Liane Rodrigues de
Bressiani,Danielle de Almeida
dc.subject.por.fl_str_mv spatio-temporal analysis
erosion tolerance index
Candiota river
topic spatio-temporal analysis
erosion tolerance index
Candiota river
description ABSTRACT Erosion process occurs naturally, shaping the Earth’s surface. Soil loss can cause harmful effects to the environment when intensive anthropic activities occur. Mathematical models have been used as effective and less costly alternatives for identifying sites highly prone to soil loss, especially at the watershed scale. In Brazil, the Revised Universal Soil Loss Equation (RUSLE) is one of the most commonly used soil loss prediction models. The RUSLE requires information on soil erodibility, rainfall erosivity, topography, land use and cover (C), and conservation practices (P) to estimate average annual soil losses. Images derived from remote sensing techniques are generally used to quantify the spatialization of C factor; however, the variation in land use throughout the year is not usually considered. This study aimed to estimate soil losses in an important subwatershed of Candiota river watershed (CRWsub) by using RUSLE, considering land use and rainfall erosivity in different periods of the year. The periods considered were P1 (January, February and March), P2 (April, May and June), P3 (July, August and September) and P4 (October, November and December). Based on the results, the lowest soil losses occurred in P1. Probably, the high vegetation cover in the soil increases its protection against rainfall erosivity. In P3, the heavy rainfall events are predominantly frontal, occurring in the same months as those when the preparation of the soil for later planting takes place; that is, there is no vegetation cover in this period, thus making the soil more prone to erosion. The use of different images to classify and identify land uses is the best way to understand soil losses throughout the year in the study area. It was possible to observe that agricultural areas are generally associated with greater soil losses in the subwatershed. In addition, the land uses were considered to vary quarterly, thereby making it possible to identify the periods most prone to erosion processes throughout the year. Finally, the erosion percentages in the subwatershed can be linked to the tolerance index for different land-uses, soil classes, and slope categories.
publishDate 2021
dc.date.none.fl_str_mv 2021-01-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832021000100521
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832021000100521
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.36783/18069657rbcs20210050
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Sociedade Brasileira de Ciência do Solo
publisher.none.fl_str_mv Sociedade Brasileira de Ciência do Solo
dc.source.none.fl_str_mv Revista Brasileira de Ciência do Solo v.45 2021
reponame:Revista Brasileira de Ciência do Solo (Online)
instname:Sociedade Brasileira de Ciência do Solo (SBCS)
instacron:SBCS
instname_str Sociedade Brasileira de Ciência do Solo (SBCS)
instacron_str SBCS
institution SBCS
reponame_str Revista Brasileira de Ciência do Solo (Online)
collection Revista Brasileira de Ciência do Solo (Online)
repository.name.fl_str_mv Revista Brasileira de Ciência do Solo (Online) - Sociedade Brasileira de Ciência do Solo (SBCS)
repository.mail.fl_str_mv ||sbcs@ufv.br
_version_ 1752126522777403392